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    <title>Research in Progress (RIP)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Developing a Standardized Framework for Real-Time Freight-Specific Traveler Information and Route Restrictions for Commercial Motor Vehicle Operators; Truck Parking Data Exchange Standards</title>
      <link>https://rip.trb.org/View/2709247</link>
      <description><![CDATA[Commercial motor vehicle (CMV) operations increasingly rely on maps and navigation systems that were not designed to address the unique needs of freight operations. This mismatch contributes to increased safety risks, including unplanned diversions, bridge strikes, congestion in freight corridors, lane geometry constraints, and other routing errors.

Today, the lack of a standard, consistent data structure or framework for sharing real-time freight-specific information remains a foundational challenge for public agencies and for the economy that depends heavily on the national roadway network. Public agencies currently lack a widely accepted standard or shared framework for communicating restrictions, alerts, and disruptions to CMV operators. Existing standards such as the Traffic Management Data Dictionary (TMDD) and SAE J2354 (Advanced Traveler Information Systems) support general traveler messaging but do not include freight-specific data elements.

In addition, the growing need for timely and reliable truck parking information, coupled with the rapid expansion of truck parking information systems, demonstrates the need for standardized methods to collect and disseminate truck parking data. As technologies used in these systems become increasingly ubiquitous, and as industry expectations and preferences continue to evolve, standardization of both information and dissemination tools becomes a critical next step.

OBJECTIVES: The objectives of this research are: (1) to develop a unified data framework for delivering time-sensitive, relevant, and actionable freight-specific traveler information messaging to CMV operators; and (2) to develop proposed data standards for real-time, public and private truck parking availability and attributes (including the number of spaces, size, hours of availability, and available amenities).

]]></description>
      <pubDate>Tue, 02 Jun 2026 14:33:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709247</guid>
    </item>
    <item>
      <title>How Do People Receive Information About Public Transit?</title>
      <link>https://rip.trb.org/View/2696850</link>
      <description><![CDATA[Providing transit information helps passengers adapt when service is unreliable and has been shown to decrease wait times, reduce overall travel time, increase ridership, increase satisfaction with transit, and increase perceptions of personal security. However, to date, there is limited evidence for how riders prefer to access and use transit information. A variety of methods are available, including websites, apps, signage, and transit ambassadors or drivers, but which methods of information are most used by riders and how does it differ by the type of rider and type of trip? Riders need real time information to be accurate, but how does inaccurate information impact their trip? In addition, how do riders plan their travel pre-trip, such as understanding hours and frequency of service, finding the stop, and understanding payment mechanism? This research aims to explore how both transit riders and non-riders access public transit information for the purpose of planning and taking trips on transit to answer these questions. This work will improve understanding of customer perspective to aid agencies in providing better transit rider information in a cost-effective manner, thus improving the long-term viability of the transportation system by increasing demand for transit.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:12:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696850</guid>
    </item>
    <item>
      <title>Development of a Real-Time Decision Support Framework for Resilient Bridge Infrastructure During Evolving Hazard Conditions
</title>
      <link>https://rip.trb.org/View/2696159</link>
      <description><![CDATA[Bridge infrastructure serves as a critical lifeline for transportation, emergency response, and economic continuity. In hazard-prone regions such as Florida, bridges face escalating risks from floods, hurricanes, and wildfires that can rapidly disrupt traffic flow and delay emergency operations. Existing bridge management systems primarily focus on long-term planning and condition assessment, offering limited capability for real-time decision-making during evolving hazard events. This project aims to develop a real-time decision support framework that enables dynamic management of bridge infrastructure under active hazard conditions. The proposed framework will integrate real-time hazard forecasts, sensor-based condition monitoring, and infrastructure performance data to guide rapid, data driven decisions. Using advanced analytics and scenario modeling, the system will support time-sensitive operational actions such as rerouting, temporary reinforcement, and emergency closures. A visual decision-support interface will convey hazard progression, bridge condition, and recommended response strategies to transportation agencies and emergency managers in an intuitive, spatially enabled format. Building upon prior work at Florida A&M University on the IntelliViz prioritization platform, this research extends the concept from long term resilience planning to operational support. A regional case study in Florida will demonstrate the practical implementation of the framework and its benefits for improving coordination, minimizing downtime, and enhancing public safety during flood and hurricane events. By integrating real-time data streams with predictive modeling and visualization tools, the project will bridge the gap between static risk assessment and dynamic hazard response, providing a scalable and implementable framework for strengthening transportation resilience and supporting informed, timely decisions during extreme events.]]></description>
      <pubDate>Mon, 27 Apr 2026 20:01:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696159</guid>
    </item>
    <item>
      <title>Enhancing Rural Public Transportation Through Community Engagement and Technology</title>
      <link>https://rip.trb.org/View/2652178</link>
      <description><![CDATA[Rural public transportation in the United States faces persistent challenges due to low population densities, inadequate infrastructure, and limited mobility options. This project aims to address these issues by leveraging advanced technologies, including digital twins and mobile applications, to enhance transit planning, scheduling, and efficiency. The study will focus on rural Texas, exploring innovative transportation models that incorporate a mix of fixed-route transit, autonomous vehicles, transportation network companies, and on-demand services tailored to meet community needs. A key component of the project is the development of a digital twin, a virtual representation of the rural transportation network, to simulate and optimize transit operations. By integrating real-time data from mobile platforms, the digital twin will enable planners to test different service configurations, predict ridership patterns, and enhance accessibility, particularly for older adults and individuals with disabilities. This approach will facilitate cost-effective, demand-responsive transit solutions that enhance mobility and improve quality of life. Community engagement is central to the project, ensuring that transportation solutions align with the needs of residents. Public meetings and stakeholder discussions will guide decision-making, while performance metrics, including user adoption, service coverage, and cost efficiency, will assess the effectiveness of the implemented strategies. The outcomes of this study will provide a replicable framework for rural mobility solutions, demonstrating how digital tools and participatory planning can transform public transit systems in rural and low-density areas. ]]></description>
      <pubDate>Tue, 13 Jan 2026 15:14:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652178</guid>
    </item>
    <item>
      <title>OpenRoad Link: A Public-Private Data Exchange for Safer, Smarter Trucking </title>
      <link>https://rip.trb.org/View/2646948</link>
      <description><![CDATA[Work zones, lane closures, and traffic incidents significantly impact roadway safety and efficiency. When lanes are blocked due to construction, crashes, or other disruptions, roadways no longer function as designed—leading unexpected congestion, increased crash risk, and reduced operational reliability. Many work zones are established to perform critical maintenance on aging infrastructure—essential to improving durability and extending the service life of roadways—but they also introduce temporary risks and delays that must be better managed.  Effects of lane blockages are particularly severe for commercial motor vehicles (CMVs), which require more time and space to slow or reroute and are subject to strict hours-of-service regulations that make delays especially costly. 

This project proposes to develop and evaluate a data exchange framework—OpenRoad Link—to integrate and share real-time lane closure, work zone, and incident data from the Oklahoma Department of Transportation (ODOT), the Oklahoma City and Tulsa Traffic Operations Centers (TOCs), and other key transportation and traffic enforcement partners. To build this framework, the project will first identify and assess the roadway data already collected and shared by these agencies, as well as the types of information currently accessible to the CMV industry through private telematics platforms. Building on national standards such as the Work Zone Data Exchange and SAE J2735 (the standard message set for vehicle-to-everything communications), the project will extend the data scope to include lane-blocking crashes, maintenance activities, and other short-term or unplanned restrictions not currently emphasized in existing feeds. Through collaboration with ODOT, city TOCs, and trucking industry partners—including a pilot with a major trucking company such as ABF—the project will demonstrate the delivery of curated, high-value information directly to in-cab devices or fleet management systems.  

Key tasks will include identifying and cataloging roadway and incident data currently collected by the Oklahoma Department of Transportation (ODOT) and the Traffic Operations Centers (TOCs) of Oklahoma City and Tulsa, as well as evaluating what information is already being shared with the commercial vehicle industry through private telematics platforms. The project will establish partnerships with ODOT, city transportation and public safety agencies, and private industry stakeholders to design and implement a unified, standards-compliant data exchange framework. Following the design phase, the team will develop and deploy the OpenRoad Link data feed, ensuring compliance with existing national standards and verifying data accuracy and reliability. A pilot deployment will be conducted in collaboration with a trucking company using a selected in-cab device to deliver actionable, real-time information directly to CMV drivers.  

Anticipated outcomes include improved safety for CMV drivers, a reduction in secondary crashes, enhanced freight reliability, and a validated proof-of-concept for scalable public-private data exchange. By producing a replicable model for collaboration between state DOTs and private-sector technology providers, the project aims to accelerate national adoption of interoperable safety data systems and promote safer, more efficient freight transportation. ]]></description>
      <pubDate>Tue, 06 Jan 2026 08:59:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646948</guid>
    </item>
    <item>
      <title>Enhancing Structural Safety and Promoting Equity in Infrastructure Maintenance through Human-Centered Bridge Inspection empowered by Artificial Intelligence and Augmented Reality
</title>
      <link>https://rip.trb.org/View/2627937</link>
      <description><![CDATA[Bridges are crucial civil infrastructure, but their deterioration over time poses significant safety risks. Traditional human visual inspections are limited in accuracy and efficiency, leading to challenges in maintaining the inventory of bridges in the United States, particularly in economically disadvantaged communities. Leveraging recent advancements in computer vision (CV), artificial intelligence (AI), and augmented reality (AR), the team proposes a novel human-centered approach to enhance the accuracy and efficiency of concrete bridge inspections and promote equity in infrastructure maintenance. By automating detection and documentation of damage in concrete bridges, and empowering human inspectors by overlaying real-time detection results onto bridges thereby enabling human-machine collaboration, the project aims to improve inspection effectiveness and efficiency, promote equity in infrastructure maintenance, and enhance public safety.
]]></description>
      <pubDate>Fri, 21 Nov 2025 14:16:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2627937</guid>
    </item>
    <item>
      <title>Evaluation of Vehicle Telematics and Infrastructure-based Connected Vehicle Data for Real-Time Safety and Mobility Application
</title>
      <link>https://rip.trb.org/View/2625309</link>
      <description><![CDATA[The emergence of connected vehicle (CV) data has provided unprecedented opportunities for developing real-time, proactive applications to enhance safety and mobility. This project utilizes and compares telematics and infrastructure-based CV data to determine optimal applications for each and explore integration strategies for safety and mobility solutions. Specifically, telematics CV data provide the location, speed, and other key information on approximately 5-10% of vehicles on the road. In contrast, infrastructure-based CV data from the connected corridor in the City of Madison contain information about traffic signals, vehicles, and road geometry. By comparing and integrating these data sources, this project proposes physics models and neural network algorithms to detect real-time safety issues such as crashes. The detection results can be used to issue immediate warnings to drivers, traffic managers, and automated vehicle systems. To disseminate these warnings, the research team proposes utilizing roadside variable message signs and in-app notifications via platforms like HAAS, Google Maps, and Waze. The proposed applications can be piloted through field tests in the University of Wisconsin-Madison’s Level 3 CAV testbed and possibly later at Mcity.]]></description>
      <pubDate>Thu, 13 Nov 2025 15:31:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625309</guid>
    </item>
    <item>
      <title>Effectiveness and Benefits of Connected Work Zones
</title>
      <link>https://rip.trb.org/View/2603850</link>
      <description><![CDATA[Many Infrastructure Owner Operators (IOOs) have begun to adopt technologies to broadcast near real-time information about the location and state of work zones and maintenance activities within their jurisdictions. While these technologies have seen continued improvement in the information that they are sharing, it is unknown how that information is able to be translated into motorist safety and internal benefits for the deploying agency. The Ohio Department of Transportation (ODOT) and DriveOhio are interested in gaining additional data showing actual safety improvement numbers related to the deployment of these technologies and measurable statistics that define the internal benefits to the agency and their relation to the agencies list of Event Streaming Platform use cases. 

OBJECTIVE: The goal of this research is to provide data and associated recommendations relating to the deployment of connected work zone technologies within the state. This research will benefit ODOT by determining the benefits, both internal and external, that connected work zone technologies enable for the safety and efficiency of roadside workers and the motoring public. Thie results of this project will help inform the direction that ODOT will take in the area of connected work zones in the future. 
]]></description>
      <pubDate>Thu, 25 Sep 2025 11:24:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2603850</guid>
    </item>
    <item>
      <title>Real-time Surface Monitoring for Improved Safety, Response, And Repair</title>
      <link>https://rip.trb.org/View/2593995</link>
      <description><![CDATA[The recent, rapid, and overwhelming movement of the Hooskanaden landslide in 2019 and the Arizona Inn landslide in early 2023 greatly disrupted traffic along US 101 with several days of full road closure followed by prolonged reduced capacity and weeks of traffic control for repairs. Alarmingly, numerous precarious landslides exist throughout the state that can result in similar consequences, triggered by precipitation or erosion—both of which will be exacerbated with climate change. Real-time, on-site instrumentation is essential to characterize landslide kinematics as well as detect and predict movements that can disrupt the highway system. Real-time, on-site instrumentation can also help inform the timing of repair and estimation of material needs, improving both on-site safety and maintenance costs associated with repair. However, the standard methodology for landslide instrumentation requires costly drilling beneath the earth’s surface—which is frequently unsafe, costly, and infeasible on an active landslide. Further, drilled subsurface instrumentation is oftentimes destroyed with landslide movement, providing only short-term usefulness for obtaining active landslide data. This project will develop and deploy low-cost surface monitoring strategies to monitor landslide movements, leveraging Oregon Department of Transportation's (ODOT's) recent proof-of-concept success using real-time kinematic global navigation satellite systems (RTK-GNSS) to monitor the Arizona Inn landslide failure, which enabled real-time delivery of critical information to help inform closure actions and repairs. Further development and establishment of surface monitoring methods will also inform statewide characterization of active slides that impact ODOT infrastructure where drilling is cost prohibitive, unsafe, or impossible.]]></description>
      <pubDate>Thu, 28 Aug 2025 15:09:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2593995</guid>
    </item>
    <item>
      <title>Real-time Continuous Bridge Scour Monitoring for Improved Safety and Cost Savings</title>
      <link>https://rip.trb.org/View/2592197</link>
      <description><![CDATA[Current Oregon Department of Transportation (ODOT) methods for monitoring bridge scour are time-consuming, labor intensive, not always accurate, very dangerous to perform during extreme storm events, and unrealistic to apply to all of the scour critical bridges. Development of a deployable remote real-time monitoring system could alleviate these issues as well as provide an early warning system for Region and District personnel. This project will (1) develop methods for identifying scour early and safely using deployable automated Real-Time Scour monitoring systems, (2) use data collected to improve bridge design, and (3) provide a planning matrix to address the scour critical bridge inventory with this advanced real-time technology.]]></description>
      <pubDate>Fri, 22 Aug 2025 14:19:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2592197</guid>
    </item>
    <item>
      <title>Harnessing Real-Time Weather Data for Improved Bridge Inspection after Flood Events</title>
      <link>https://rip.trb.org/View/2590605</link>
      <description><![CDATA[The research aim is to harness near real-time weather data to improve guidance for bridge inspections following rainfall events. Recent rainfall events in Indiana have washed out bridges, resulting in broken transportation networks and loss of life. The National Oceanic and Atmospheric Administration (NOAA) provides near real-time weather information that integrates data from radar, rain gauges, satellites, numerical predictions, and other observations (i.e., lightning, surface, upper air). This research will investigate the use of this publicly available data to trigger bridge inspections, with the aim of improving
public safety.
]]></description>
      <pubDate>Tue, 19 Aug 2025 15:02:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2590605</guid>
    </item>
    <item>
      <title>Continuous Friction Measuring Equipment Assessment and Testing Results for Florida Pavement</title>
      <link>https://rip.trb.org/View/2571988</link>
      <description><![CDATA[The objective of this research study is to develop a continuous friction measurements program (CFMP) in Florida to enhance road safety and optimize road maintenance by providing continuous real-time data on road surface friction, geometry, and related safety conditions.]]></description>
      <pubDate>Mon, 07 Jul 2025 09:36:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2571988</guid>
    </item>
    <item>
      <title>Creation of a Rapidly-Updating Road Weather Impacts Prediction System (RWIPS)</title>
      <link>https://rip.trb.org/View/2548657</link>
      <description><![CDATA[This proposal lays the framework for an end-to-end decision-support capability for the 
Missouri Department of Transportation (MODOT) that provides potentially impactful weather information coupled with a routing decision tool to aid in effective response to road weather hazards.  The project is a multi-stage one that will leverage expertise within NOAA and OU/CIWRO for real-time weather monitoring and forecasting and then expand upon capabilities developed and demonstrated by Missouri University Science and Technology.]]></description>
      <pubDate>Wed, 30 Apr 2025 09:20:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2548657</guid>
    </item>
    <item>
      <title>Development of a Scalable, Low-Cost, Environmentally-Friendly Adaptive Traffic Signal Control (SLE-ATSC) System</title>
      <link>https://rip.trb.org/View/2495005</link>
      <description><![CDATA[As an enhanced method for vehicle detection at signalized intersections, it is possible to use vehicle-probe data from smartphones, Global Navigation Satellite System (GNSS) receivers, and other types of mobile devices to complement existing traffic sensing and signal control, resulting in lower energy consumption. Using these additional data, it is now possible to estimate reliable traffic queue lengths at high-density traffic intersections. Given real-time reliable traffic queue lengths, it is possible then to dynamically adjust the signal phase and timing of an intersection, with the goal of minimizing traffic queues, waiting times, and energy use. Using UC Riverside’s Innovation Corridor as a target arterial roadway, the research team will develop a scalable, low-cost, environmentally-friendly adaptive traffic signal control (SLE-ATSC) system based on receiving real-time traffic data from sources such as TomTom and INRIX. The signal control system will be implemented for several of the key intersections along the corridor, using a calibrated state-of-the-art traffic simulation platform. Various metrics will be evaluated, comparing the existing traffic signal phase and timing to the new dynamic signal phase and timing resulting from the adaptive signal control system. Using the calibrated simulation model, traffic system metrics will be estimated. In addition, part of the research team (TSU) will utilize their driving simulators as part of a “Hardware-in- the-Loop” testing system for the proposed adaptive traffic signal control system. The traffic simulation model developed at UCR will interface directly with the TSU driving simulators, allowing the research team to see more realistic driving behavior operating in the simulation platform. This will provide more realistic measures of the overall system performance, with a focus on safety, mobility, and environmental metrics.]]></description>
      <pubDate>Fri, 31 Jan 2025 16:35:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2495005</guid>
    </item>
    <item>
      <title>AI-Powered Tools for Safe Evacuation of Individuals During Emergencies</title>
      <link>https://rip.trb.org/View/2431741</link>
      <description><![CDATA[Emergency evacuation via transportation systems and infrastructure is a critical component of public safety, particularly in scenarios involving fires, transportation incidents, wildfires, and tsunamis. Studies have demonstrated the potential of artificial intelligence (AI) and machine learning to enhance evacuation efficiency. The first research need is to investigate and create personalized evacuation plans that consider various factors such as mobility limitations, communication needs, and access to resources. The second research need is to explore methods for integrating real-time data from IoT (internet of things) devices into AI-powered evacuation tools. First, the research team will assemble comprehensive datasets from multiple sources, focusing on evacuation scenarios involving fires, transportation incidents, wildfires, and tsunamis. Second, the team will employ a range of advanced analytical methods to synthesize and analyze the collected data, ensuring the development of effective AI-powered evacuation tools. This research is expected to yield significant advancements in the modeling, practices, and procedures for emergency evacuations, particularly for vulnerable populations.]]></description>
      <pubDate>Sat, 21 Sep 2024 15:31:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2431741</guid>
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